Wednesday, March 1, 2017

adjusting my mental model: movement correlation after preprocessing

It's time to adjust my mental model of the fMRI signal: there's a lot more correlation with movement in the timecourses after preprocessing than I'd expected. That movement really affects fMRIis notat allnew, of course, and is why including the motion regressors as covariates in GLMs is standard. But I'd pictured that after preprocessing (assuming it went well and included realignment and spatial normalization) the correlation with movement left in the voxel timecourses should be pretty low (something like normally distributed, centered on 0, ranging between -0.2 to 0.2), without much spatial structure (i.e., maybe some ringing, but fairly uniformly present over the brain). Asking around, I think this is a fairly common mental model, but it looks to be quite wrong.

For exploring, I simply used afni's 3dTcorr1D program to correlate the timecourse of every voxel in several preprocessed task fMRI datasets with each of the six motion regressors (generated during preprocessing). 3dTcorr1D makes an image with 6 entries in the 4th dimension (sub-brick, in afni-speak), one for each of the 6 motion columns; the value in each voxel the Pearson correlation between that voxel's timecourse and the movement column. I plotted these correlations on brains, and made histograms to summarize the distribution.

The correlations are much higher than I expected, even in people with
very little movement. Here's an example; more follow. Below are the motion regressors from single task run (about 12.5 minutes long; HCP-style preprocessing; MB8 protocol), the correlation with each motion regressor, and
a (density-style) histogram of the voxel-wise correlations. Color scaling for this and all brain images is from 1 (hottest) to -1 (coolest), not showing correlations between -0.25 and 0.25.

If my expectation (correlations normally distributed, centered on 0, ranging between -0.2 to 0.2) was right, there shouldn't be any color on these images at all, but there's clearly quite a bit: many voxels correlate around 0.5 with roll and pitch (4th and 5th brain rows are mostly "hot" colors), and around -0.5 with x, y, and z (first three rows mostly "cool" colors). There's some structure to the peak correlations (e.g., a hotter strip along the left side in slice 46), which may correspond with sulci or large vessels, but it's rather speckly. Note that this is a pretty low motion subject overall: less than 2 mm drift over the 12 minute run, and only 4 volumes marked for censoring (I didn't censor before the correlation).

Looking at other people and datasets, including from non-SMS acquisitions with larger voxels and longer TRs, it appears like correlations of 0.5 are pretty common: this isn't just some sort of weird effect that only shows up with high-resolution acquisitions. For another example, these are the histograms and motion regressors for four runs from one person included in this study (acquired with 4 mm isotropic voxels; run duration 7.6 min, TR 2.5 sec, SPM preprocessing). The corresponding brain images are below the jump.

So, really visible motion (which at least sometimes is linked to respiration) in the voxel activity timecourses (such as here) is to be expected. Unfortunately, the correlation is not always (or even usually) uniform across the brain or grey matter, such as below (just the correlation with x and y translation). It also looks like very little (under a mm) motion is needed to induce large correlations.

What to do? Well, adjust our mental models of how much correlation with movement is left in the activation timeseries after preprocessing: there's quite a bit. I'll be exploring further, particularly isolating the task windows (since I work with task, not resting state, datsets): how are the correlations during tasks? I'm not at all sure that applying a global signal regression-type step would be beneficial, given the lack of homogeneity across the brain (though I know there are at least a few reports on using it with task data). Censoring high-movement trials (i.e., not including them) is likely sensible. Interestingly, I've found similar MVPA performance in multiple cases with temporal compression by averaging and fitting a model (PEIs), which would not have been my guess looking at these correlation levels. Perhaps averaging across enough timepoints and trials balances out some of the residual motion effects? I am concerned, however, about respiration (and motion) effects remaining in the timecourses: it's clear that some people adjust their breathing to task timing, and we don't want to be interpreting a breath-holding effect as due to motivation.

Any other thoughts or experiences? Are you surprised by these correlation levels, or is it what you've already known?

Voxel-wise correlations plotted on brains, for the same "behemoth" participant whose motion and histograms are shown above. There is a lot of correlation outside the brain, since I did not apply a brain mask during the preprocessing (unlike the HCP pipelines). The histograms were generated using voxels from within the brain mask only, however.

11 comments:

Hi Jo, great post! One issue that I would like to look at when I have time is rigid body realignment versus affine realignment. In the breathing experiments we did last year we could easily see the shearing effects produced from chest motion. The rigid body realignment should fail in this case, and this will always be at least some of the effect of head & chest motion. Moreover we can expect variable effects across the brain, because the magnetic field is non-uniform. If you get a chance to test an affine correction, that would be very interesting. May still be insufficient because it would presumably be a volume correction and slice corrections may be needed, but still might be illuminating.

Checking the SPM8 preprocessing settings I used for the "behemoth" study in this post, I did have the default, nonlinear settings for normalization. I'll have to review how exactly the pipelines are set up for the SMS datasets.

I wonder how the strength of this correlation changes after high-pass filtering is applied? Perhaps that is done in the HCP processing pipeline, but I'm not sure. I do know that in SPM, I have looked at an F contrast examining the main effect of all 6 motion parameters and typically it is rather shocking how much is 'significantly' related, even after high-pass filtering.

Good question! Checking my notes, I didn't do high-pass filtering on the SPM8-processed dataset in this post. I think I have some datasets sitting around that were filtered, though; might be interesting to try with those.

Looks like there isn't high-pass filtering in the HCP preprocessing pipelines, either. Filtering is usually done during the GLM, and so after the "preprocessed" datasets I included here, but this is an intriguing idea.

I think a big problem is related to spin history artifacts, which are very hard to resolve in software. The best method is still to obtain data in which subjects simply do not move either by locking the head or by scanner adjustments to acquisition schemes based on movement.

very interesting blog. when reading the blog, I have one point in my mind. actually, here 'head motion' would not be the pure head motion, as the measures are usually extracted from the fMRI data. when doing the motion correction, we get the head motion measures as a by-product. Given fMRI data is somehow noisy, the motion correction largely depend on the intensity of the fMRI data, which may reflect not only head motion but also meaningful regional activity. Do you think this would be a potential source of the unexpected high correlation between motion and fmri data?

Scanner-fixed contrast associated with receive coil arrays is known to cause motion-related error in fMRI data. Was the data acquired using a 32 channel head coil? Did you click the prescan normalization option on your Siemens scanner when acquiring the data? It would be very interesting to repeat your analysis using an 8 or 12 channel coil with prescan normalization both on and off.

We had the prescan normalization off for the first examples (higher resolution, MB8), which were collected on a Siemens Prisma with a 32 channel head coil. The other ("behemoth") dataset was collected something like 10 years ago on a Siemens Allegra; I don't have the coil info on hand. I don't have any datasets with prescan normalization on & off, but it's easy enough to run 3dTcorr1D if someone else has any.

So the behemoth set was probably acquired using a 4-channel transmit/receive head array. As a 4-channel receive coil the scanner-fixed contrast would probably be reduced compared to the 32-channel data but still significant. Since the transmit coil is 4-channel this might exacerbate the scanner-fixed contrast problem compared to systems using a body coil for transmit (like the Trio).